HPC-ABDS: The Case for an Integrating Apache Big Data Stack with HPC 1st JTC 1 SGBD Meeting SDSC San Diego March 19 2014 Judy Qiu Shantenu Jha (Rutgers) Geoffrey Fox gcf@indiana.edu http://www.infomall.org School of Informatics and Computing Digital Science Center Indiana University Bloomington

Enhanced Apache Big Data Stack ABDS ~120 Capabilities >40 Apache Green layers have strong HPC Integration opportunities Goal Functionality of ABDS Performance of HPC

Broad Layers in HPC-ABDS Workflow-Orchestration Application and Analytics High level Programming Basic Programming model and runtime SPMD, Streaming, MapReduce, MPI Inter process communication Collectives, point to point, publish-subscribe In memory databases/caches Object-relational mapping SQL and NoSQL, File management Data Transport Cluster Resource Management (Yarn, Slurm, SGE) File systems(HDFS, Lustre …) DevOps (Puppet, Chef …) IaaS Management from HPC to hypervisors (OpenStack) Cross Cutting Message Protocols Distributed Coordination Security & Privacy Monitoring

Getting High Performance on Data Analytics (e.g. Mahout, R …) On the systems side, we have two principles The Apache Big Data Stack with ~120 projects has important broad functionality with a vital large support organization HPC including MPI has striking success in delivering high performance with however a fragile sustainability model There are key systems abstractions which are levels in HPC-ABDS software stack where Apache approach needs careful integration with HPC Resource management Storage Programming model -- horizontal scaling parallelism Collective and Point to Point communication Support of iteration Data interface (not just key-value) In application areas, we define application abstractions to support Graphs/network  Geospatial Images etc.

4 Forms of MapReduce   (a) Map Only (d) Loosely Synchronous (c) Iterative MapReduce (b) Classic MapReduce Input map reduce Iterations Output Pij BLAST Analysis Parametric sweep Pleasingly Parallel High Energy Physics (HEP) Histograms Distributed search Classic MPI PDE Solvers and particle dynamics Domain of MapReduce and Iterative Extensions Science Clouds MPI Giraph Expectation maximization Clustering e.g. Kmeans Linear Algebra, Page Rank MPI is Map followed by Point to Point or Collective Communication – as in style c) plus d)

HPC-ABDS Hourglass HPC ABDS System (Middleware) 120 Software Projects High performance Applications HPC-ABDS Hourglass 120 Software Projects System Abstractions/standards Data format Storage HPC Yarn for Resource management Horizontally scalable parallel programming model Collective and Point to Point communication Support of iteration Application Abstractions/standards Graphs, Networks, Images, Geospatial …. SPIDAL (Scalable Parallel Interoperable Data Analytics Library) or High performance Mahout, R, Matlab …..

Integrating Yarn with HPC

We are sort of working on Use Cases with HPC-ABDS Use Case 10 Internet of Things: Yarn, Storm, ActiveMQ Use Case 19, 20 Genomics. Hadoop, Iterative MapReduce, MPI, Much better analytics than Mahout Use Case 26 Deep Learning. High performance distributed GPU (optimized collectives) with Python front end (planned) Variant of Use Case 26, 27 Image classification using Kmeans: Iterative MapReduce Use Case 28 Twitter with optimized index for Hbase, Hadoop and Iterative MapReduce Use Case 30 Network Science. MPI and Giraph for network structure and dynamics (planned) Use Case 39 Particle Physics. Iterative MapReduce (wrote proposal) Use Case 43 Radar Image Analysis. Hadoop for multiple individual images moving to Iterative MapReduce for global integration over “all” images Use Case 44 Radar Images. Running on Amazon

Features of Harp Hadoop Plug in Hadoop Plugin (on Hadoop 1.2.1 and Hadoop 2.2.0) Hierarchical data abstraction on arrays, key-values and graphs for easy programming expressiveness. Collective communication model to support various communication operations on the data abstractions. Caching with buffer management for memory allocation required from computation and communication BSP style parallelism Fault tolerance with check-pointing

Architecture MapReduce Applications Map-Collective Applications YARN MapReduce V2 Harp MapReduce Applications Map-Collective Applications Application Framework Resource Manager

Performance on Madrid Cluster (8 nodes) Increasing Communication Identical Computation Note compute same in each case as product of centers times points identical

Mahout and Hadoop MR – Slow due to MapReduce Python slow as Scripting Spark Iterative MapReduce, non optimal communication Harp Hadoop plug in with ~MPI collectives MPI fastest as C not Java Increasing Communication Identical Computation

Performance of MPI Kernel Operations Pure Java as in FastMPJ slower than Java interfacing to C version of MPI

Use case 28: Truthy: Information diffusion research from Twitter Data Building blocks: Yarn Parallel query evaluation using Hadoop MapReduce Related hashtag mining algorithm using Hadoop MapReduce: Meme daily frequency generation using MapReduce over index tables Parallel force-directed graph layout algorithm using Twister (Harp) iterative MapReduce

Use case 28: Truthy: Information diffusion research from Twitter Data Two months’ data loading for varied cluster size Scalability of iterative graph layout algorithm on Twister Hadoop-FS not indexed

Pig Performance Hadoop Harp-Hadoop Pig +HD1 (Hadoop) Pig + Yarn

Lines of Code Pig Kmeans Hadoop Kmeans Pig IndexedHBase   Pig Kmeans Hadoop Kmeans Pig IndexedHBase meme-cooccur-count IndexedHBase Java ~345 780 152 ~434 Pig 10 Python / Bash ~40 28 Total Lines 395 162 462

DACIDR for Gene Analysis (Use Case 19,20) Deterministic Annealing Clustering and Interpolative Dimension Reduction Method (DACIDR) Use Hadoop for pleasingly parallel applications, and Twister (replacing by Yarn) for iterative MapReduce applications Sequences – Cluster  Centers Add Existing data and find Phylogenetic Tree Pairwise Clustering All-Pair Sequence Alignment Streaming Visualization Multidimensional Scaling Simplified Flow Chart of DACIDR

Summarize a million Fungi Sequences Spherical Phylogram Visualization Spherical Phylogram from new MDS method visualized in PlotViz RAxML result visualized in FigTree.

Lessons / Insights Integrate (don’t compete) HPC with “Commodity Big data” (Google to Amazon to Enterprise data Analytics) i.e. improve Mahout; don’t compete with it Use Hadoop plug-ins rather than replacing Hadoop Enhanced Apache Big Data Stack HPC-ABDS has 120 members – please improve! HPC-ABDS+ Integration areas include file systems, cluster resource management, file and object data management, inter process and thread communication, analytics libraries, Workflow monitoring